{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29e66c1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (962, 5)\n",
      "\n",
      "前5行数据:\n",
      "    关键词                            标题  \\\n",
      "0  华能信托            信托公司2019年上半年经营业绩概览   \n",
      "1  华能信托                  首单信托型企业ABS获批   \n",
      "2  华能信托       华能贵诚信托孙磊:金融科技助力打造开放信托生态   \n",
      "3  华能信托  华能贵诚信托孙磊:金融科技已经成为信托行业重要的基础设施   \n",
      "4  华能信托      格力电器股权转让意向方闭门开会 华能信托赫然在列   \n",
      "\n",
      "                                                  网址       来源  \\\n",
      "0  http://www.financialnews.com.cn/jrsb_m/xt/zx/2...  中国金融新闻网   \n",
      "1     http://www.jjckb.cn/2018-10/23/c_137552198.htm    经济参考网   \n",
      "2  https://baijiahao.baidu.com/s?id=1639276579449...    同花顺财经   \n",
      "3       https://finance.qq.com/a/20190716/007898.htm     腾讯财经   \n",
      "4  https://finance.sina.com.cn/trust/roll/2019-05...       新浪   \n",
      "\n",
      "                  时间  \n",
      "0  2019年07月23日 00:00  \n",
      "1  2018年10月23日 12:21  \n",
      "2  2019年07月17日 10:49  \n",
      "3  2019年07月16日 18:53  \n",
      "4  2019年05月22日 22:53  \n",
      "\n",
      "分词示例:\n",
      "原始标题: 信托公司2019年上半年经营业绩概览\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Dumping model to file cache C:\\Users\\Lenovo\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.506 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词结果: 信托公司 2019 年 上半年 经营 业绩 概览\n",
      "\n",
      "正在进行中文分词...\n",
      "前3条新闻的分词结果:\n",
      "1. 信托公司 2019 年 上半年 经营 业绩 概览\n",
      "2. 首单 信托 型 企业 ABS 获批\n",
      "3. 华能 贵 诚信 托孙磊 : 金融 科技 助力 打造 开放 信托 生态\n",
      "\n",
      "============================================================\n",
      "文本向量化\n",
      "============================================================\n",
      "\n",
      "使用 词频统计 进行向量化...\n",
      "词袋大小: 3402\n",
      "特征矩阵形状: (962, 3402)\n",
      "前10个特征词: ['00700' '03' '04' '08s' '09' '10' '100' '11' '12' '150']\n",
      "\n",
      "使用 TF-IDF 进行向量化...\n",
      "词袋大小: 3402\n",
      "特征矩阵形状: (962, 3402)\n",
      "前10个特征词: ['00700' '03' '04' '08s' '09' '10' '100' '11' '12' '150']\n",
      "\n",
      "============================================================\n",
      "聚类算法比较\n",
      "============================================================\n",
      "\n",
      "进行PCA降维以便可视化...\n",
      "PCA降维后形状: (962, 2)\n",
      "PCA解释方差比: [0.01651101 0.00849829]\n",
      "\n",
      "=== KMeans ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "轮廓系数: 0.0184\n",
      "Calinski-Harabasz指数: 8.0033\n",
      "簇分布: {0: 96, 1: 607, 2: 87, 3: 78, 4: 94}\n",
      "\n",
      "=== 层次聚类 ===\n",
      "聚类失败: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.\n",
      "\n",
      "=== 谱聚类 ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "轮廓系数: 0.0280\n",
      "Calinski-Harabasz指数: 8.8787\n",
      "簇分布: {0: 896, 1: 43, 2: 7, 3: 9, 4: 7}\n",
      "\n",
      "=== 高斯混合模型 ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "轮廓系数: 0.0089\n",
      "Calinski-Harabasz指数: 4.8282\n",
      "簇分布: {0: 76, 1: 192, 2: 581, 3: 21, 4: 92}\n",
      "\n",
      "=== DBSCAN ===\n",
      "轮廓系数: 0.0597\n",
      "Calinski-Harabasz指数: 7.3539\n",
      "簇分布: {-1: 899, 0: 7, 1: 7, 2: 7, 3: 7, 4: 7, 5: 7, 6: 7, 7: 7, 8: 7}\n",
      "\n",
      "============================================================\n",
      "聚类算法性能比较\n",
      "============================================================\n",
      "       算法      轮廓系数  Calinski指数  簇数量\n",
      "3  DBSCAN  0.059732    7.353873   10\n",
      "1     谱聚类  0.027993    8.878726    5\n",
      "0  KMeans  0.018411    8.003294    5\n",
      "2  高斯混合模型  0.008870    4.828229    5\n",
      "\n",
      "============================================================\n",
      "聚类结果可视化\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "最佳聚类算法: DBSCAN\n",
      "============================================================\n",
      "\n",
      "各簇关键词分析:\n",
      "\n",
      "--- 簇 -1 (包含 899 条新闻) ---\n",
      "关键词: 人工智能, 科技, 腾讯, 文化, 阿里巴巴\n",
      "示例标题:\n",
      "  - 信托公司2019年上半年经营业绩概览\n",
      "  - 首单信托型企业ABS获批\n",
      "  - 华能贵诚信托孙磊:金融科技助力打造开放信托生态\n",
      "\n",
      "--- 簇 0 (包含 7 条新闻) ---\n",
      "关键词: 信托, 王卓, 接任, 首任, 辞职\n",
      "示例标题:\n",
      "  - 五矿信托首任总经理辞职 接任者或为华能信托王卓\n",
      "  - 五矿信托首任总经理辞职 接任者或为华能信托王卓\n",
      "  - 五矿信托首任总经理辞职 接任者或为华能信托王卓\n",
      "\n",
      "--- 簇 1 (包含 7 条新闻) ---\n",
      "关键词: 公告, 幸福, 华夏, 关于, 签署\n",
      "示例标题:\n",
      "  - 华夏幸福关于拟与华能信托签署《增资协议》的公告\n",
      "  - 华夏幸福关于拟与华能信托签署《增资协议》的公告\n",
      "  - 华夏幸福关于拟与华能信托签署《增资协议》的公告\n",
      "\n",
      "--- 簇 2 (包含 7 条新闻) ---\n",
      "关键词: 普邦, 信托, 集合, 资金, 合同\n",
      "示例标题:\n",
      "  - 普邦股份:华能信托.普邦1号集合资金信托计划信托合同\n",
      "  - 普邦股份:华能信托.普邦1号集合资金信托计划信托合同\n",
      "  - 普邦股份:华能信托.普邦1号集合资金信托计划信托合同\n",
      "\n",
      "--- 簇 3 (包含 7 条新闻) ---\n",
      "关键词: 46, 不良资产, 银登, 试水, 收益权\n",
      "示例标题:\n",
      "  - 华能信托试水首单不良资产收益权转让已有46家信托公司与银登中心...\n",
      "  - 华能信托试水首单不良资产收益权转让已有46家信托公司与银登中心...\n",
      "  - 华能信托试水首单不良资产收益权转让已有46家信托公司与银登中心...\n",
      "\n",
      "--- 簇 4 (包含 7 条新闻) ---\n",
      "关键词: 信托, 中航, 慈善, 携手, 模式\n",
      "示例标题:\n",
      "  - 北京银行携手华能、中航信托创新慈善信托模式\n",
      "  - 北京银行携手华能、中航信托创新慈善信托模式\n",
      "  - 北京银行携手华能、中航信托创新慈善信托模式\n",
      "\n",
      "--- 簇 5 (包含 7 条新闻) ---\n",
      "关键词: 领袖, 学员, 第一期, acca, 选拔\n",
      "示例标题:\n",
      "  - ACCA-华能信托“财经领袖培养计划”第一期学员选拔结果公布\n",
      "  - ACCA-华能信托“财经领袖培养计划”第一期学员选拔结果公布\n",
      "  - ACCA-华能信托“财经领袖培养计划”第一期学员选拔结果公布\n",
      "\n",
      "--- 簇 6 (包含 7 条新闻) ---\n",
      "关键词: 有限公司, 诚信, 华能\n",
      "示例标题:\n",
      "  - 华能贵诚信托有限公司\n",
      "  - 华能贵诚信托有限公司\n",
      "  - 华能贵诚信托有限公司\n",
      "\n",
      "--- 簇 7 (包含 7 条新闻) ---\n",
      "关键词: abs, 发行, 国金, 招行, 早报\n",
      "示例标题:\n",
      "  - 国金ABS云 · 早报丨招行与华能信托将合作发行99亿元ABS\n",
      "  - 国金ABS云 · 早报丨招行与华能信托将合作发行99亿元ABS\n",
      "  - 国金ABS云 · 早报丨招行与华能信托将合作发行99亿元ABS\n",
      "\n",
      "--- 簇 8 (包含 7 条新闻) ---\n",
      "关键词: 信托, 净利, 日报, 用益, 排位\n",
      "示例标题:\n",
      "  - 用益-信托日报:平安江苏中信华能位列前四!58家信托上半年净利排位!\n",
      "  - 用益-信托日报:平安江苏中信华能位列前四!58家信托上半年净利排位!\n",
      "  - 用益-信托日报:平安江苏中信华能位列前四!58家信托上半年净利排位!\n",
      "\n",
      "============================================================\n",
      "不同向量化方法比较\n",
      "============================================================\n",
      "\n",
      "使用 词频统计:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  轮廓系数: 0.0211\n",
      "  簇分布: {0: 798, 1: 69, 2: 87, 3: 1, 4: 7}\n",
      "\n",
      "使用 TF-IDF:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  轮廓系数: 0.0184\n",
      "  簇分布: {0: 96, 1: 607, 2: 87, 3: 78, 4: 94}\n",
      "\n",
      "============================================================\n",
      "聚类数量选择分析\n",
      "============================================================\n",
      "不同K值的聚类效果:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=2: 惯性值=939.32, 轮廓系数=0.0126\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=3: 惯性值=933.35, 轮廓系数=0.0143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=4: 惯性值=928.12, 轮廓系数=0.0159\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=5: 惯性值=922.78, 轮廓系数=0.0184\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=6: 惯性值=916.51, 轮廓系数=0.0207\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=7: 惯性值=910.26, 轮廓系数=0.0233\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=8: 惯性值=904.95, 轮廓系数=0.0255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=9: 惯性值=898.59, 轮廓系数=0.0287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=10: 惯性值=895.52, 轮廓系数=0.0288\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "最佳聚类结果总结\n",
      "============================================================\n",
      "推荐聚类数量: K = 10\n",
      "对应的轮廓系数: 0.0288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\pppython\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "聚类完成! 新闻被分为 10 个类别\n",
      "聚类标签已添加到原始数据中\n",
      "\n",
      "文本聚类分析完成!\n"
     ]
    }
   ],
   "source": [
    "# 设置中文字体支持\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1.读取数据\n",
    "import pandas as pd\n",
    "df = pd.read_excel(\"D:\\贺之恒的python\\pppython\\python商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第13章 数据聚类与分群\\源代码汇总_PyCharm格式\\新闻.xlsx\")\n",
    "print(\"数据形状:\", df.shape)\n",
    "print(\"\\n前5行数据:\")\n",
    "print(df.head())\n",
    "\n",
    "# 2.中文分词\n",
    "import jieba\n",
    "\n",
    "# 显示分词示例\n",
    "print(\"\\n分词示例:\")\n",
    "print(\"原始标题:\", df.iloc[0]['标题'])\n",
    "word_example = jieba.cut(df.iloc[0]['标题'])\n",
    "result_example = ' '.join(word_example)\n",
    "print(\"分词结果:\", result_example)\n",
    "\n",
    "# 对所有标题进行分词\n",
    "print(\"\\n正在进行中文分词...\")\n",
    "words = []\n",
    "for i, row in df.iterrows():\n",
    "    word = jieba.cut(row['标题'])\n",
    "    result = ' '.join(word)\n",
    "    words.append(result)\n",
    "\n",
    "print(\"前3条新闻的分词结果:\")\n",
    "for i in range(3):\n",
    "    print(f\"{i+1}. {words[i]}\")\n",
    "\n",
    "# 3.文本向量化：通过建立词频矩阵构造特征变量\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"文本向量化\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 使用两种不同的向量化方法\n",
    "vectorizers = {\n",
    "    '词频统计': CountVectorizer(),\n",
    "    'TF-IDF': TfidfVectorizer()\n",
    "}\n",
    "\n",
    "X_vectors = {}\n",
    "\n",
    "for vec_name, vectorizer in vectorizers.items():\n",
    "    print(f\"\\n使用 {vec_name} 进行向量化...\")\n",
    "    X = vectorizer.fit_transform(words)\n",
    "    X_vectors[vec_name] = X\n",
    "    \n",
    "    # 获取特征名称\n",
    "    feature_names = vectorizer.get_feature_names_out()\n",
    "    print(f\"词袋大小: {len(feature_names)}\")\n",
    "    print(f\"特征矩阵形状: {X.shape}\")\n",
    "    \n",
    "    # 显示前10个特征词\n",
    "    print(f\"前10个特征词: {feature_names[:10]}\")\n",
    "\n",
    "# 4.使用不同的聚类算法\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering, SpectralClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, adjusted_rand_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"聚类算法比较\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 选择TF-IDF向量化结果进行聚类（通常效果更好）\n",
    "X = X_vectors['TF-IDF']\n",
    "\n",
    "# 由于文本数据高维稀疏，先进行PCA降维以便可视化\n",
    "print(\"\\n进行PCA降维以便可视化...\")\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X.toarray())\n",
    "\n",
    "print(f\"PCA降维后形状: {X_pca.shape}\")\n",
    "print(f\"PCA解释方差比: {pca.explained_variance_ratio_}\")\n",
    "\n",
    "# 定义聚类算法\n",
    "n_clusters = 5  # 假设分为5个类别\n",
    "\n",
    "clustering_algorithms = {\n",
    "    'KMeans': KMeans(n_clusters=n_clusters, random_state=42),\n",
    "    '层次聚类': AgglomerativeClustering(n_clusters=n_clusters),\n",
    "    '谱聚类': SpectralClustering(n_clusters=n_clusters, random_state=42),\n",
    "    '高斯混合模型': GaussianMixture(n_components=n_clusters, random_state=42),\n",
    "    'DBSCAN': DBSCAN(eps=0.5, min_samples=5)\n",
    "}\n",
    "\n",
    "# 存储聚类结果\n",
    "results = {}\n",
    "\n",
    "# 5.执行聚类分析\n",
    "for algo_name, algorithm in clustering_algorithms.items():\n",
    "    print(f\"\\n=== {algo_name} ===\")\n",
    "    \n",
    "    try:\n",
    "        # 执行聚类\n",
    "        if algo_name == '高斯混合模型':\n",
    "            # GMM需要概率模型\n",
    "            labels = algorithm.fit_predict(X.toarray())\n",
    "        else:\n",
    "            labels = algorithm.fit_predict(X)\n",
    "        \n",
    "        # 计算评估指标\n",
    "        if len(np.unique(labels)) > 1:  # 确保有多个簇\n",
    "            silhouette = silhouette_score(X, labels)\n",
    "            calinski = calinski_harabasz_score(X.toarray(), labels)\n",
    "        else:\n",
    "            silhouette = -1\n",
    "            calinski = -1\n",
    "        \n",
    "        # 统计每个簇的样本数量\n",
    "        unique, counts = np.unique(labels, return_counts=True)\n",
    "        cluster_dist = dict(zip(unique, counts))\n",
    "        \n",
    "        print(f\"轮廓系数: {silhouette:.4f}\")\n",
    "        print(f\"Calinski-Harabasz指数: {calinski:.4f}\")\n",
    "        print(f\"簇分布: {cluster_dist}\")\n",
    "        \n",
    "        # 存储结果\n",
    "        results[algo_name] = {\n",
    "            'labels': labels,\n",
    "            'silhouette': silhouette,\n",
    "            'calinski': calinski,\n",
    "            'cluster_distribution': cluster_dist\n",
    "        }\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"聚类失败: {e}\")\n",
    "        continue\n",
    "\n",
    "# 6.比较聚类性能\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"聚类算法性能比较\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "if results:\n",
    "    performance_df = pd.DataFrame({\n",
    "        '算法': list(results.keys()),\n",
    "        '轮廓系数': [result['silhouette'] for result in results.values()],\n",
    "        'Calinski指数': [result['calinski'] for result in results.values()],\n",
    "        '簇数量': [len(result['cluster_distribution']) for result in results.values()]\n",
    "    }).sort_values('轮廓系数', ascending=False)\n",
    "    \n",
    "    print(performance_df)\n",
    "\n",
    "# 7.可视化聚类结果\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"聚类结果可视化\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 选择性能最好的算法\n",
    "if results:\n",
    "    best_algo_name = performance_df.iloc[0]['算法']\n",
    "    best_labels = results[best_algo_name]['labels']\n",
    "    \n",
    "    # 创建可视化\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "    axes = axes.flatten()\n",
    "    \n",
    "    # 绘制每个算法的聚类结果\n",
    "    for idx, (algo_name, result) in enumerate(list(results.items())[:6]):\n",
    "        if idx >= len(axes):\n",
    "            break\n",
    "            \n",
    "        labels = result['labels']\n",
    "        \n",
    "        # 使用PCA降维后的数据进行可视化\n",
    "        scatter = axes[idx].scatter(X_pca[:, 0], X_pca[:, 1], \n",
    "                                   c=labels, cmap='viridis', alpha=0.7, s=30)\n",
    "        axes[idx].set_title(f'{algo_name}\\n轮廓系数: {result[\"silhouette\"]:.3f}', \n",
    "                           fontsize=12, fontweight='bold')\n",
    "        axes[idx].set_xlabel('主成分1')\n",
    "        axes[idx].set_ylabel('主成分2')\n",
    "        plt.colorbar(scatter, ax=axes[idx])\n",
    "    \n",
    "    # 如果子图数量不足，隐藏多余的子图\n",
    "    for idx in range(len(results), len(axes)):\n",
    "        axes[idx].set_visible(False)\n",
    "    \n",
    "    plt.suptitle('不同聚类算法的结果比较（PCA降维可视化）', \n",
    "                fontsize=16, fontweight='bold')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 8.最佳算法的详细分析\n",
    "if results:\n",
    "    print(f\"\\n\" + \"=\"*60)\n",
    "    print(f\"最佳聚类算法: {best_algo_name}\")\n",
    "    print(\"=\"*60)\n",
    "    \n",
    "    best_result = results[best_algo_name]\n",
    "    best_labels = best_result['labels']\n",
    "    \n",
    "    # 将聚类结果添加到原始数据中\n",
    "    df['聚类标签'] = best_labels\n",
    "    \n",
    "    # 分析每个簇的关键词\n",
    "    print(\"\\n各簇关键词分析:\")\n",
    "    \n",
    "    # 获取向量化器\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    X_tfidf = vectorizer.fit_transform(words)\n",
    "    feature_names = vectorizer.get_feature_names_out()\n",
    "    \n",
    "    # 对每个簇分析关键词\n",
    "    for cluster_id in np.unique(best_labels):\n",
    "        cluster_indices = np.where(best_labels == cluster_id)[0]\n",
    "        cluster_size = len(cluster_indices)\n",
    "        \n",
    "        print(f\"\\n--- 簇 {cluster_id} (包含 {cluster_size} 条新闻) ---\")\n",
    "        \n",
    "        if cluster_size > 0:\n",
    "            # 计算该簇中每个词的TF-IDF平均值\n",
    "            cluster_tfidf = X_tfidf[cluster_indices].mean(axis=0)\n",
    "            cluster_tfidf = np.array(cluster_tfidf).flatten()\n",
    "            \n",
    "            # 获取最重要的10个词\n",
    "            top_indices = cluster_tfidf.argsort()[-10:][::-1]\n",
    "            top_words = [feature_names[i] for i in top_indices if cluster_tfidf[i] > 0]\n",
    "            top_scores = [cluster_tfidf[i] for i in top_indices if cluster_tfidf[i] > 0]\n",
    "            \n",
    "            print(\"关键词:\", ', '.join(top_words[:5]))\n",
    "            \n",
    "            # 显示该簇的示例标题\n",
    "            print(\"示例标题:\")\n",
    "            for i in cluster_indices[:3]:  # 显示前3个示例\n",
    "                print(f\"  - {df.iloc[i]['标题']}\")\n",
    "\n",
    "# 9.使用不同向量化方法的比较\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"不同向量化方法比较\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 比较词频统计和TF-IDF的效果\n",
    "best_algo = KMeans(n_clusters=n_clusters, random_state=42)  # 使用KMeans作为基准\n",
    "\n",
    "vectorizer_results = {}\n",
    "\n",
    "for vec_name, X_vec in X_vectors.items():\n",
    "    print(f\"\\n使用 {vec_name}:\")\n",
    "    \n",
    "    try:\n",
    "        labels = best_algo.fit_predict(X_vec)\n",
    "        \n",
    "        if len(np.unique(labels)) > 1:\n",
    "            silhouette = silhouette_score(X_vec, labels)\n",
    "            print(f\"  轮廓系数: {silhouette:.4f}\")\n",
    "            \n",
    "            vectorizer_results[vec_name] = {\n",
    "                'labels': labels,\n",
    "                'silhouette': silhouette\n",
    "            }\n",
    "            \n",
    "            # 统计簇分布\n",
    "            unique, counts = np.unique(labels, return_counts=True)\n",
    "            print(f\"  簇分布: {dict(zip(unique, counts))}\")\n",
    "            \n",
    "    except Exception as e:\n",
    "        print(f\"  聚类失败: {e}\")\n",
    "\n",
    "# 10.聚类数量选择分析（肘部法则）\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"聚类数量选择分析\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 使用KMeans进行肘部法则分析\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "k_range = range(2, 11)\n",
    "inertia_scores = []\n",
    "silhouette_scores = []\n",
    "\n",
    "print(\"不同K值的聚类效果:\")\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    labels = kmeans.fit_predict(X)\n",
    "    inertia_scores.append(kmeans.inertia_)\n",
    "    \n",
    "    if len(np.unique(labels)) > 1:\n",
    "        silhouette = silhouette_score(X, labels)\n",
    "        silhouette_scores.append(silhouette)\n",
    "    else:\n",
    "        silhouette_scores.append(-1)\n",
    "    \n",
    "    print(f\"K={k}: 惯性值={kmeans.inertia_:.2f}, 轮廓系数={silhouette:.4f}\")\n",
    "\n",
    "# 绘制肘部法则图\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# 惯性值图\n",
    "ax1.plot(k_range, inertia_scores, 'bo-', linewidth=2, markersize=8)\n",
    "ax1.set_xlabel('聚类数量 K')\n",
    "ax1.set_ylabel('惯性值')\n",
    "ax1.set_title('肘部法则 - 惯性值随K值变化', fontsize=14, fontweight='bold')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# 轮廓系数图\n",
    "ax2.plot(k_range, silhouette_scores, 'ro-', linewidth=2, markersize=8)\n",
    "ax2.set_xlabel('聚类数量 K')\n",
    "ax2.set_ylabel('轮廓系数')\n",
    "ax2.set_title('轮廓系数随K值变化', fontsize=14, fontweight='bold')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 11.最佳聚类结果展示\n",
    "if results:\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(\"最佳聚类结果总结\")\n",
    "    print(f\"{'='*60}\")\n",
    "    \n",
    "    # 选择轮廓系数最好的K值\n",
    "    best_k_index = np.argmax(silhouette_scores)\n",
    "    best_k = k_range[best_k_index]\n",
    "    best_k_silhouette = silhouette_scores[best_k_index]\n",
    "    \n",
    "    print(f\"推荐聚类数量: K = {best_k}\")\n",
    "    print(f\"对应的轮廓系数: {best_k_silhouette:.4f}\")\n",
    "    \n",
    "    # 使用最佳K值重新聚类\n",
    "    final_kmeans = KMeans(n_clusters=best_k, random_state=42)\n",
    "    final_labels = final_kmeans.fit_predict(X)\n",
    "    \n",
    "    # 可视化最终聚类结果\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    scatter = plt.scatter(X_pca[:, 0], X_pca[:, 1], c=final_labels, \n",
    "                         cmap='tab10', alpha=0.7, s=50)\n",
    "    plt.xlabel('主成分1')\n",
    "    plt.ylabel('主成分2')\n",
    "    plt.title(f'最终聚类结果 (K={best_k}, 轮廓系数={best_k_silhouette:.3f})', \n",
    "              fontsize=14, fontweight='bold')\n",
    "    plt.colorbar(scatter)\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 保存最终结果\n",
    "    df['最终聚类标签'] = final_labels\n",
    "    print(f\"\\n聚类完成! 新闻被分为 {best_k} 个类别\")\n",
    "    print(\"聚类标签已添加到原始数据中\")\n",
    "\n",
    "print(\"\\n文本聚类分析完成!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b65e88a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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